Name
Abstract | ||
|---|---|---|
We introduce a segmentation-guided approach to synthesise images that integrate features from two distinct domains. Images synthesised by our dual-domain model belong to one domain within the semantic-mask, and to another in the rest of the image - smoothly integrated. We build on the successes of few-shot StyleGAN and single-shot semantic segmentation to minimise the amount of training required in utilising two domains.The method combines few-shot cross-domain StyleGAN with a latent optimiser to achieve images containing features of two distinct domains. We use a segmentation-guided perceptual loss, which compares both pixel-level and activations between domain-specific and dual-domain synthetic images. Results demonstrate qualitatively and quantitatively that our model is capable of synthesising dual-domain images on a variety of objects (faces, horses, cats, cars), domains (natural, caricature, sketches) and part-based masks (eyes, nose, mouth, hair, car bonnet). The code is publicly available
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Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPRW56347.2022.00066 | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Keywords | DocType | ISSN |
dual-domain image synthesis,semantic-mask,single-shot semantic segmentation,few-shot cross-domain StyleGAN,segmentation-guided perceptual loss,segmentation-guided GAN | Conference | 2160-7508 |
ISBN | Citations | PageRank |
978-1-6654-8740-5 | 0 | 0.34 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dena Bazazian | 1 | 0 | 0.34 |
| Andrew Calway | 2 | 645 | 54.66 |
| Dima Damen | 3 | 225 | 31.54 |